The AI-Based Cyber Threat Landscape: A Survey
Bibliographic record
Abstract
Recent advancements in artificial intelligence (AI) technologies have induced tremendous growth in innovation and automation. Although these AI technologies offer significant benefits, they can be used maliciously. Highly targeted and evasive attacks in benign carrier applications, such as DeepLocker, have demonstrated the intentional use of AI for harmful purposes. Threat actors are constantly changing and improving their attack strategy with particular emphasis on the application of AI-driven techniques in the attack process, called AI-based cyber attack, which can be used in conjunction with conventional attack techniques to cause greater damage. Despite several studies on AI and security, researchers have not summarized AI-based cyber attacks enough to be able to understand the adversary’s actions and to develop proper defenses against such attacks. This study aims to explore existing studies of AI-based cyber attacks and to map them onto a proposed framework, providing insight into new threats. Our framework includes the classification of several aspects of malicious uses of AI during the cyber attack life cycle and provides a basis for their detection to predict future threats. We also explain how to apply this framework to analyze AI-based cyber attacks in a hypothetical scenario of a critical smart grid infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".